University of Kentucky

MA 721: Topics in Numerical Analysis: Deep Learning

Fall 2017

MWF 1:00 pm - 1:50 pm, CB 347


     Dr. Qiang Ye
     Office: 735 Patterson Office Tower
     Email: qye3 "at" uky . edu
     Office Hours:  MWF 2:00-3:00 pm


     There will be no required text, but the following books will be good references:


     In this course, we study a widely applicable class of machine learning methods called deep learning. We will cover the following topics:      Selected materials from optimization, linear algebra, and probability theory/information theory will be covered.


     Familiarity with multivariate calculus, linear algebra and numerical methods will be assumed. Programming in Python will be required.


     The course grade will be based on programming projects (60%) and an in-class presentation (40%).

Course Materials


     We will use Python based toolboxs such as Keras, Theano, or Tensorflow. You may choose to use any of them. It is best to run them on the Linux platform. But if you use Windows, below are some links for setting up Keras/Theano/Tensorflow on Windows 10. You may also install these toolboxes using ANACONDA DISTRIBUTION. Download the distribution and try the following (Thanks to Liu Liu for these tips!)

Some references and links

     Below are some links and books on numerical linear algebra, optimization, and machine learning that may be helpful.